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        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        About MultiQC

        This report was generated using MultiQC, version 1.25.1

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        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the nf-core/rnaseq analysis pipeline. For information about how to interpret these results, please see the documentation.

        Report generated on 2025-06-30, 16:47 EEST based on data in: /scratch/project_2003826/SOIL2GUT_RNA/work/e5/5fd497f25fcf9cd88a8c876fdcd1ee


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        General Statistics

        Showing 0/90 rows and 20/35 columns.
        Sample Name
        dupInt
        Duplication
        5'-3' bias
        M Aligned
        Proper Pairs
        Error rate
        Non-primary
        Reads mapped
        % Mapped
        % Proper pairs
        % MapQ 0 reads
        Total seqs
        Reads
        Reads mapped
        % Reads mapped
        rRNA
        Total reads
        Aligned
        Aligned
        Uniq aligned
        Uniq aligned
        Multimapped
        Dups
        GC
        Avg len
        Median len
        Failed
        Seqs
        Trimmed bases
        Dups
        GC
        Avg len
        Median len
        Failed
        Seqs
        Control_SG37BCR
        0.1
        29.7%
        1.39
        29.5M
        64.3%
        0.63%
        3.4M
        59.1M
        100.0%
        99.8%
        0.2%
        59.1M
        62.5M
        62.5M
        100.0%
        32.1M
        29.6M
        92.3%
        28.5M
        88.7%
        1.2M
        Control_SG37BCR_1
        65.2%
        48.0%
        150bp
        150bp
        27%
        33.9M
        5.0%
        65.1%
        48.0%
        143bp
        147bp
        18%
        33.7M
        Control_SG37BCR_2
        4.2%
        64.7%
        49.0%
        150bp
        150bp
        27%
        33.9M
        4.6%
        64.6%
        48.0%
        143bp
        147bp
        27%
        33.7M
        Control_SG40ACR
        0.2
        33.0%
        1.36
        29.8M
        61.0%
        0.72%
        3.7M
        59.7M
        100.0%
        99.8%
        0.1%
        59.7M
        63.4M
        63.4M
        100.0%
        36.3M
        29.9M
        82.3%
        28.5M
        78.6%
        1.4M
        Control_SG40ACR_1
        64.7%
        47.0%
        150bp
        150bp
        36%
        43.9M
        4.9%
        64.7%
        47.0%
        143bp
        150bp
        27%
        43.9M
        Control_SG40ACR_2
        16.6%
        64.8%
        48.0%
        150bp
        150bp
        36%
        43.9M
        4.9%
        64.8%
        48.0%
        142bp
        147bp
        36%
        43.9M
        Control_SG68ACR
        0.2
        34.2%
        1.37
        41.7M
        60.0%
        0.63%
        5.0M
        83.6M
        100.0%
        99.8%
        0.2%
        83.6M
        88.6M
        88.6M
        100.0%
        48.6M
        41.9M
        86.1%
        40.2M
        82.6%
        1.7M
        Control_SG68ACR_1
        66.5%
        48.0%
        150bp
        150bp
        36%
        55.1M
        5.1%
        66.5%
        47.0%
        142bp
        150bp
        27%
        55.1M
        Control_SG68ACR_2
        10.9%
        66.6%
        48.0%
        150bp
        150bp
        27%
        55.1M
        5.1%
        66.6%
        48.0%
        142bp
        147bp
        36%
        55.1M
        Control_SG6BCR
        0.1
        30.7%
        1.16
        19.8M
        61.8%
        0.65%
        3.1M
        39.7M
        100.0%
        99.8%
        0.3%
        39.7M
        42.9M
        42.9M
        100.0%
        34.0M
        19.9M
        58.5%
        19.0M
        55.7%
        0.9M
        Control_SG6BCR_1
        67.6%
        45.0%
        150bp
        150bp
        45%
        41.0M
        5.3%
        67.6%
        44.0%
        142bp
        150bp
        36%
        40.9M
        Control_SG6BCR_2
        15.8%
        68.3%
        45.0%
        150bp
        150bp
        45%
        41.0M
        5.2%
        68.3%
        45.0%
        142bp
        147bp
        36%
        40.9M
        Control_SG7ACR
        0.1
        34.2%
        1.02
        33.6M
        58.4%
        0.63%
        5.5M
        67.3M
        100.0%
        99.8%
        0.3%
        67.3M
        72.8M
        72.8M
        100.0%
        41.4M
        33.7M
        81.5%
        32.0M
        77.4%
        1.7M
        Control_SG7ACR_1
        70.9%
        47.0%
        150bp
        150bp
        36%
        48.4M
        6.7%
        70.9%
        47.0%
        140bp
        147bp
        27%
        48.4M
        Control_SG7ACR_2
        13.9%
        71.3%
        48.0%
        150bp
        150bp
        36%
        48.4M
        6.5%
        71.3%
        48.0%
        140bp
        147bp
        36%
        48.4M
        Control_SG86ACR
        0.2
        30.7%
        1.41
        29.9M
        63.8%
        0.64%
        3.1M
        59.9M
        100.0%
        99.8%
        0.1%
        59.9M
        63.1M
        63.1M
        100.0%
        34.6M
        30.0M
        86.8%
        28.9M
        83.7%
        1.1M
        Control_SG86ACR_1
        63.2%
        48.0%
        150bp
        150bp
        27%
        37.7M
        4.2%
        63.3%
        48.0%
        144bp
        150bp
        18%
        37.7M
        Control_SG86ACR_2
        7.5%
        63.0%
        48.0%
        150bp
        150bp
        27%
        37.7M
        4.2%
        63.0%
        48.0%
        144bp
        147bp
        27%
        37.7M
        Park_SG109BCR
        0.1
        34.2%
        1.40
        42.3M
        59.8%
        0.66%
        5.4M
        84.8M
        100.0%
        99.8%
        0.1%
        84.8M
        90.2M
        90.2M
        100.0%
        47.0M
        42.5M
        90.5%
        40.4M
        86.1%
        2.1M
        Park_SG109BCR_1
        67.2%
        47.0%
        150bp
        150bp
        27%
        50.9M
        4.4%
        67.2%
        47.0%
        143bp
        150bp
        18%
        50.9M
        Park_SG109BCR_2
        7.0%
        67.1%
        48.0%
        150bp
        150bp
        27%
        50.9M
        4.3%
        67.1%
        48.0%
        143bp
        147bp
        27%
        50.9M
        Park_SG17BCR
        0.2
        33.0%
        1.36
        34.8M
        61.6%
        0.63%
        3.7M
        69.8M
        100.0%
        99.8%
        0.1%
        69.8M
        73.5M
        73.5M
        100.0%
        39.5M
        35.0M
        88.4%
        33.6M
        85.1%
        1.3M
        Park_SG17BCR_1
        65.6%
        48.0%
        150bp
        150bp
        18%
        42.3M
        3.0%
        65.6%
        48.0%
        145bp
        150bp
        18%
        42.3M
        Park_SG17BCR_2
        5.7%
        66.0%
        49.0%
        150bp
        150bp
        18%
        42.3M
        3.0%
        66.0%
        49.0%
        145bp
        150bp
        27%
        42.3M
        Park_SG18ACR
        0.1
        31.4%
        1.40
        42.5M
        63.2%
        0.70%
        4.5M
        85.1M
        100.0%
        99.9%
        0.1%
        85.1M
        89.6M
        89.6M
        100.0%
        46.6M
        42.6M
        91.5%
        41.0M
        88.0%
        1.6M
        Park_SG18ACR_1
        62.5%
        47.0%
        150bp
        150bp
        27%
        48.2M
        4.3%
        62.6%
        47.0%
        143bp
        150bp
        18%
        48.1M
        Park_SG18ACR_2
        2.6%
        61.5%
        48.0%
        150bp
        150bp
        27%
        48.2M
        4.4%
        61.5%
        48.0%
        143bp
        147bp
        27%
        48.1M
        Park_SG23ACR
        0.1
        31.3%
        1.29
        38.4M
        62.9%
        0.62%
        4.4M
        76.8M
        100.0%
        99.8%
        0.2%
        76.8M
        81.2M
        81.2M
        100.0%
        41.5M
        38.5M
        92.7%
        37.0M
        89.1%
        1.5M
        Park_SG23ACR_1
        66.4%
        48.0%
        150bp
        150bp
        27%
        43.7M
        4.0%
        66.4%
        48.0%
        144bp
        150bp
        18%
        43.6M
        Park_SG23ACR_2
        4.1%
        66.4%
        49.0%
        150bp
        150bp
        27%
        43.7M
        4.0%
        66.4%
        48.0%
        144bp
        147bp
        27%
        43.6M
        Park_SG48BCR
        0.2
        36.7%
        1.23
        26.8M
        56.9%
        0.63%
        3.8M
        53.7M
        100.0%
        99.8%
        0.2%
        53.7M
        57.5M
        57.5M
        100.0%
        37.2M
        26.9M
        72.4%
        25.7M
        69.0%
        1.3M
        Park_SG48BCR_1
        64.2%
        46.0%
        150bp
        150bp
        27%
        42.1M
        5.2%
        64.2%
        46.0%
        142bp
        147bp
        18%
        42.1M
        Park_SG48BCR_2
        10.7%
        64.3%
        47.0%
        150bp
        150bp
        27%
        42.1M
        5.0%
        64.3%
        47.0%
        142bp
        147bp
        27%
        42.1M
        Park_SG53ACR
        0.2
        31.2%
        1.31
        21.9M
        62.0%
        0.61%
        3.0M
        43.9M
        100.0%
        99.8%
        0.2%
        43.9M
        46.9M
        46.9M
        100.0%
        35.6M
        22.0M
        61.8%
        21.0M
        59.1%
        1.0M
        Park_SG53ACR_1
        60.4%
        47.0%
        150bp
        150bp
        27%
        45.5M
        5.5%
        60.4%
        46.0%
        142bp
        150bp
        18%
        45.4M
        Park_SG53ACR_2
        20.8%
        62.0%
        47.0%
        150bp
        150bp
        36%
        45.5M
        5.5%
        62.0%
        47.0%
        142bp
        147bp
        27%
        45.4M
        Park_SG73BCR
        0.1
        42.9%
        1.24
        65.7M
        50.8%
        0.63%
        11.0M
        131.7M
        100.0%
        99.8%
        0.3%
        131.7M
        142.7M
        142.7M
        100.0%
        79.8M
        66.0M
        82.7%
        62.5M
        78.3%
        3.5M
        Park_SG73BCR_1
        77.6%
        47.0%
        150bp
        150bp
        36%
        86.8M
        7.4%
        77.7%
        46.0%
        139bp
        147bp
        27%
        86.8M
        Park_SG73BCR_2
        7.2%
        76.6%
        47.0%
        150bp
        150bp
        36%
        86.8M
        7.1%
        76.6%
        47.0%
        139bp
        147bp
        36%
        86.8M
        Park_SG90ACR
        0.2
        30.1%
        1.28
        12.7M
        62.2%
        0.71%
        2.0M
        25.4M
        100.0%
        99.8%
        0.3%
        25.4M
        27.5M
        27.5M
        100.0%
        25.6M
        12.7M
        49.7%
        12.1M
        47.2%
        0.6M
        Park_SG90ACR_1
        61.9%
        47.0%
        150bp
        150bp
        45%
        40.4M
        5.5%
        61.9%
        47.0%
        142bp
        150bp
        36%
        40.4M
        Park_SG90ACR_2
        35.3%
        65.1%
        48.0%
        150bp
        150bp
        36%
        40.4M
        5.5%
        65.1%
        47.0%
        141bp
        147bp
        36%
        40.4M
        Park_SG94BCR
        0.1
        32.4%
        1.32
        43.7M
        61.9%
        0.63%
        4.9M
        87.6M
        100.0%
        99.8%
        0.2%
        87.6M
        92.6M
        92.6M
        100.0%
        47.1M
        43.9M
        93.2%
        42.2M
        89.6%
        1.7M
        Park_SG94BCR_1
        67.1%
        48.0%
        150bp
        150bp
        27%
        48.7M
        4.5%
        67.1%
        48.0%
        143bp
        150bp
        18%
        48.7M
        Park_SG94BCR_2
        2.6%
        67.0%
        49.0%
        150bp
        150bp
        27%
        48.7M
        4.6%
        67.0%
        49.0%
        143bp
        147bp
        27%
        48.7M
        Park_SG95ACR
        0.2
        32.6%
        1.32
        31.8M
        61.7%
        0.62%
        3.6M
        63.7M
        100.0%
        99.8%
        0.2%
        63.7M
        67.4M
        67.4M
        100.0%
        35.2M
        31.9M
        90.7%
        30.7M
        87.1%
        1.2M
        Park_SG95ACR_1
        64.9%
        48.0%
        150bp
        150bp
        27%
        37.7M
        5.8%
        64.9%
        48.0%
        141bp
        147bp
        18%
        37.7M
        Park_SG95ACR_2
        5.8%
        64.4%
        49.0%
        150bp
        150bp
        27%
        37.7M
        5.7%
        64.4%
        49.0%
        141bp
        147bp
        27%
        37.7M
        Urban_SG30ACR
        0.1
        32.6%
        1.39
        30.1M
        61.0%
        0.63%
        3.9M
        60.3M
        100.0%
        99.8%
        0.2%
        60.3M
        64.2M
        64.2M
        100.0%
        40.2M
        30.2M
        75.2%
        28.9M
        72.0%
        1.3M
        Urban_SG30ACR_1
        68.9%
        46.0%
        150bp
        150bp
        27%
        43.4M
        4.8%
        68.9%
        46.0%
        143bp
        150bp
        18%
        43.4M
        Urban_SG30ACR_2
        6.6%
        69.1%
        47.0%
        150bp
        150bp
        36%
        43.4M
        4.7%
        69.1%
        47.0%
        143bp
        147bp
        36%
        43.4M
        Urban_SG32BCR
        0.1
        33.3%
        1.26
        30.8M
        60.1%
        0.66%
        4.2M
        61.6M
        100.0%
        99.8%
        0.2%
        61.6M
        65.9M
        65.9M
        100.0%
        34.4M
        30.9M
        89.7%
        29.5M
        85.7%
        1.4M
        Urban_SG32BCR_1
        67.2%
        48.0%
        150bp
        150bp
        27%
        36.9M
        5.8%
        67.2%
        48.0%
        141bp
        147bp
        18%
        36.8M
        Urban_SG32BCR_2
        5.5%
        67.3%
        49.0%
        150bp
        150bp
        27%
        36.9M
        5.6%
        67.3%
        49.0%
        141bp
        147bp
        27%
        36.8M
        Urban_SG33BCR
        0.1
        30.5%
        1.38
        32.8M
        63.7%
        0.64%
        3.7M
        65.7M
        100.0%
        99.8%
        0.2%
        65.7M
        69.4M
        69.4M
        100.0%
        36.5M
        32.9M
        90.1%
        31.7M
        86.7%
        1.2M
        Urban_SG33BCR_1
        64.3%
        48.0%
        150bp
        150bp
        27%
        39.0M
        4.0%
        64.3%
        48.0%
        144bp
        150bp
        18%
        39.0M
        Urban_SG33BCR_2
        5.5%
        63.9%
        48.0%
        150bp
        150bp
        27%
        39.0M
        3.9%
        64.0%
        48.0%
        144bp
        150bp
        27%
        39.0M
        Urban_SG34BCR
        0.1
        34.2%
        1.33
        49.5M
        59.9%
        0.67%
        6.2M
        99.1M
        100.0%
        99.8%
        0.2%
        99.1M
        105.3M
        105.3M
        100.0%
        54.5M
        49.6M
        91.1%
        47.5M
        87.2%
        2.1M
        Urban_SG34BCR_1
        70.4%
        48.0%
        150bp
        150bp
        27%
        56.7M
        5.8%
        70.4%
        48.0%
        141bp
        147bp
        18%
        56.7M
        Urban_SG34BCR_2
        3.3%
        70.0%
        49.0%
        150bp
        150bp
        27%
        56.7M
        5.6%
        70.0%
        48.0%
        141bp
        147bp
        27%
        56.7M
        Urban_SG55BCR
        0.2
        34.3%
        1.30
        35.4M
        59.5%
        0.74%
        4.8M
        71.0M
        100.0%
        99.8%
        0.2%
        71.0M
        75.8M
        75.8M
        100.0%
        44.0M
        35.5M
        80.7%
        33.8M
        76.9%
        1.7M
        Urban_SG55BCR_1
        66.8%
        46.0%
        150bp
        150bp
        27%
        49.8M
        4.7%
        66.8%
        46.0%
        143bp
        150bp
        18%
        49.8M
        Urban_SG55BCR_2
        10.8%
        66.0%
        47.0%
        150bp
        150bp
        27%
        49.8M
        4.5%
        66.0%
        46.0%
        143bp
        147bp
        27%
        49.8M
        Urban_SG56BCR
        0.1
        32.7%
        1.35
        34.2M
        61.0%
        0.60%
        4.4M
        68.5M
        100.0%
        99.8%
        0.2%
        68.5M
        72.9M
        72.9M
        100.0%
        39.0M
        34.3M
        88.1%
        32.9M
        84.3%
        1.5M
        Urban_SG56BCR_1
        67.5%
        48.0%
        150bp
        150bp
        27%
        42.4M
        4.8%
        67.5%
        48.0%
        143bp
        150bp
        18%
        42.4M
        Urban_SG56BCR_2
        7.4%
        67.9%
        48.0%
        150bp
        150bp
        27%
        42.4M
        4.7%
        67.9%
        48.0%
        143bp
        147bp
        27%
        42.4M
        Urban_SG62ACR
        0.1
        34.3%
        1.32
        40.1M
        59.4%
        0.62%
        5.4M
        80.5M
        100.0%
        99.8%
        0.2%
        80.5M
        85.8M
        85.8M
        100.0%
        49.8M
        40.3M
        80.9%
        38.6M
        77.5%
        1.7M
        Urban_SG62ACR_1
        68.5%
        48.0%
        150bp
        150bp
        27%
        55.4M
        5.2%
        68.5%
        48.0%
        142bp
        150bp
        27%
        55.4M
        Urban_SG62ACR_2
        9.1%
        68.7%
        49.0%
        150bp
        150bp
        27%
        55.4M
        5.0%
        68.7%
        48.0%
        142bp
        147bp
        27%
        55.4M
        Urban_SG64BCR
        0.1
        31.7%
        1.17
        34.3M
        61.0%
        0.61%
        5.3M
        68.7M
        100.0%
        99.8%
        0.3%
        68.7M
        74.0M
        74.0M
        100.0%
        40.3M
        34.4M
        85.3%
        32.8M
        81.3%
        1.6M
        Urban_SG64BCR_1
        65.5%
        49.0%
        150bp
        150bp
        27%
        45.6M
        8.6%
        65.6%
        48.0%
        137bp
        147bp
        18%
        45.6M
        Urban_SG64BCR_2
        10.6%
        66.8%
        49.0%
        150bp
        150bp
        27%
        45.6M
        8.5%
        66.8%
        48.0%
        137bp
        147bp
        36%
        45.6M
        Urban_SG78ACR
        0.2
        30.5%
        1.13
        14.7M
        61.4%
        0.61%
        2.5M
        29.4M
        100.0%
        99.8%
        0.3%
        29.4M
        31.9M
        31.9M
        100.0%
        38.8M
        14.7M
        38.0%
        14.0M
        36.1%
        0.7M
        Urban_SG78ACR_1
        82.6%
        43.0%
        150bp
        150bp
        36%
        47.3M
        6.9%
        82.7%
        43.0%
        140bp
        147bp
        27%
        47.2M
        Urban_SG78ACR_2
        17.4%
        82.7%
        44.0%
        150bp
        150bp
        55%
        47.3M
        6.9%
        82.7%
        43.0%
        139bp
        147bp
        45%
        47.2M
        Urban_SG79ACR
        0.1
        32.0%
        1.30
        31.4M
        61.2%
        0.64%
        4.4M
        62.8M
        100.0%
        99.9%
        0.2%
        62.8M
        67.2M
        67.2M
        100.0%
        33.9M
        31.4M
        92.9%
        30.0M
        88.6%
        1.5M
        Urban_SG79ACR_1
        69.2%
        48.0%
        150bp
        150bp
        27%
        35.1M
        4.2%
        69.2%
        47.0%
        144bp
        150bp
        18%
        35.1M
        Urban_SG79ACR_2
        2.8%
        68.7%
        48.0%
        150bp
        150bp
        27%
        35.1M
        4.1%
        68.7%
        48.0%
        144bp
        147bp
        27%
        35.1M
        Urban_SG80BCR
        0.1
        34.4%
        1.30
        33.0M
        59.5%
        0.71%
        4.3M
        66.1M
        100.0%
        99.8%
        0.2%
        66.1M
        70.4M
        70.4M
        100.0%
        36.5M
        33.1M
        90.6%
        31.5M
        86.4%
        1.5M
        Urban_SG80BCR_1
        64.8%
        47.0%
        150bp
        150bp
        27%
        38.3M
        4.5%
        64.8%
        46.0%
        143bp
        150bp
        18%
        38.2M
        Urban_SG80BCR_2
        3.8%
        64.4%
        47.0%
        150bp
        150bp
        27%
        38.3M
        4.2%
        64.4%
        47.0%
        143bp
        147bp
        27%
        38.2M
        Urban_SG82ACR
        0.1
        31.6%
        1.32
        18.6M
        61.7%
        0.64%
        2.5M
        37.3M
        100.0%
        99.7%
        0.2%
        37.3M
        39.8M
        39.8M
        100.0%
        27.1M
        18.7M
        69.0%
        17.9M
        65.9%
        0.8M
        Urban_SG82ACR_1
        58.8%
        46.0%
        150bp
        150bp
        36%
        31.9M
        5.9%
        58.8%
        45.0%
        141bp
        147bp
        27%
        31.8M
        Urban_SG82ACR_2
        13.4%
        58.2%
        46.0%
        150bp
        150bp
        36%
        31.9M
        5.8%
        58.2%
        46.0%
        141bp
        147bp
        36%
        31.8M
        Urban_SG84ACR
        0.2
        32.9%
        1.39
        37.6M
        61.1%
        0.61%
        4.6M
        75.4M
        100.0%
        99.8%
        0.2%
        75.4M
        80.0M
        80.0M
        100.0%
        47.5M
        37.8M
        79.5%
        36.2M
        76.2%
        1.5M
        Urban_SG84ACR_1
        61.6%
        47.0%
        150bp
        150bp
        27%
        53.2M
        5.8%
        61.6%
        47.0%
        141bp
        147bp
        27%
        53.2M
        Urban_SG84ACR_2
        9.7%
        62.3%
        48.0%
        150bp
        150bp
        36%
        53.2M
        5.9%
        62.3%
        48.0%
        141bp
        147bp
        36%
        53.2M
        Urban_SG97BCR
        0.1
        33.3%
        1.30
        39.3M
        60.3%
        0.63%
        5.3M
        78.7M
        100.0%
        99.8%
        0.2%
        78.7M
        83.9M
        83.9M
        100.0%
        44.7M
        39.4M
        88.2%
        37.7M
        84.4%
        1.7M
        Urban_SG97BCR_1
        69.7%
        48.0%
        150bp
        150bp
        27%
        47.7M
        6.1%
        69.8%
        48.0%
        141bp
        147bp
        18%
        47.7M
        Urban_SG97BCR_2
        5.4%
        70.2%
        49.0%
        150bp
        150bp
        27%
        47.7M
        5.9%
        70.2%
        48.0%
        141bp
        147bp
        27%
        47.7M

        Sample status checks

        Reports on sample strandedness status, and any failures in trimming or mapping.

        Strandedness Checks

        Showing 0/60 rows and 7/7 columns.
        Sample
        Status
        Strand inference method
        Provided strandedness
        Inferred strandedness
        Sense (%)
        Antisense (%)
        Unstranded (%)
        Control_SG37BCR
        pass
        RSeQC
        auto
        reverse
        2.4
        94.2
        3.4
        Control_SG37BCR
        pass
        Salmon
        auto
        reverse
        1.2
        98.8
        0.0
        Control_SG40ACR
        pass
        RSeQC
        auto
        reverse
        2.5
        94.0
        3.4
        Control_SG40ACR
        pass
        Salmon
        auto
        reverse
        1.5
        98.5
        0.0
        Control_SG68ACR
        pass
        RSeQC
        auto
        reverse
        2.7
        93.7
        3.7
        Control_SG68ACR
        pass
        Salmon
        auto
        reverse
        1.4
        98.6
        0.0
        Control_SG6BCR
        pass
        RSeQC
        auto
        reverse
        3.2
        94.3
        2.5
        Control_SG6BCR
        pass
        Salmon
        auto
        reverse
        1.5
        98.5
        0.0
        Control_SG7ACR
        pass
        RSeQC
        auto
        reverse
        2.6
        94.0
        3.5
        Control_SG7ACR
        pass
        Salmon
        auto
        reverse
        1.4
        98.6
        0.0
        Control_SG86ACR
        pass
        RSeQC
        auto
        reverse
        2.2
        94.5
        3.3
        Control_SG86ACR
        pass
        Salmon
        auto
        reverse
        1.4
        98.6
        0.0
        Park_SG109BCR
        pass
        RSeQC
        auto
        reverse
        2.7
        93.1
        4.2
        Park_SG109BCR
        pass
        Salmon
        auto
        reverse
        1.4
        98.6
        0.0
        Park_SG17BCR
        pass
        RSeQC
        auto
        reverse
        0.4
        96.4
        3.3
        Park_SG17BCR
        pass
        Salmon
        auto
        reverse
        1.5
        98.5
        0.0
        Park_SG18ACR
        pass
        RSeQC
        auto
        reverse
        0.6
        95.0
        4.4
        Park_SG18ACR
        pass
        Salmon
        auto
        reverse
        1.4
        98.6
        0.0
        Park_SG23ACR
        pass
        RSeQC
        auto
        reverse
        0.4
        95.3
        4.2
        Park_SG23ACR
        pass
        Salmon
        auto
        reverse
        1.2
        98.8
        0.0
        Park_SG48BCR
        pass
        RSeQC
        auto
        reverse
        2.5
        94.3
        3.2
        Park_SG48BCR
        pass
        Salmon
        auto
        reverse
        1.7
        98.3
        0.0
        Park_SG53ACR
        pass
        RSeQC
        auto
        reverse
        3.2
        93.7
        3.0
        Park_SG53ACR
        pass
        Salmon
        auto
        reverse
        1.3
        98.7
        0.0
        Park_SG73BCR
        pass
        RSeQC
        auto
        reverse
        0.8
        94.5
        4.7
        Park_SG73BCR
        pass
        Salmon
        auto
        reverse
        1.7
        98.3
        0.0
        Park_SG90ACR
        pass
        RSeQC
        auto
        reverse
        2.3
        95.5
        2.2
        Park_SG90ACR
        pass
        Salmon
        auto
        reverse
        1.6
        98.4
        0.0
        Park_SG94BCR
        pass
        RSeQC
        auto
        reverse
        0.6
        95.0
        4.3
        Park_SG94BCR
        pass
        Salmon
        auto
        reverse
        1.3
        98.7
        0.0
        Park_SG95ACR
        pass
        RSeQC
        auto
        reverse
        2.5
        94.3
        3.2
        Park_SG95ACR
        pass
        Salmon
        auto
        reverse
        1.3
        98.7
        0.0
        Urban_SG30ACR
        pass
        RSeQC
        auto
        reverse
        2.2
        94.7
        3.2
        Urban_SG30ACR
        pass
        Salmon
        auto
        reverse
        1.4
        98.6
        0.0
        Urban_SG32BCR
        pass
        RSeQC
        auto
        reverse
        0.7
        96.6
        2.7
        Urban_SG32BCR
        pass
        Salmon
        auto
        reverse
        1.8
        98.2
        0.0
        Urban_SG33BCR
        pass
        RSeQC
        auto
        reverse
        2.4
        93.3
        4.3
        Urban_SG33BCR
        pass
        Salmon
        auto
        reverse
        1.3
        98.7
        0.0
        Urban_SG34BCR
        pass
        RSeQC
        auto
        reverse
        0.7
        94.2
        5.1
        Urban_SG34BCR
        pass
        Salmon
        auto
        reverse
        1.4
        98.6
        0.0
        Urban_SG55BCR
        pass
        RSeQC
        auto
        reverse
        3.0
        92.1
        4.9
        Urban_SG55BCR
        pass
        Salmon
        auto
        reverse
        1.5
        98.5
        0.0
        Urban_SG56BCR
        pass
        RSeQC
        auto
        reverse
        0.5
        95.9
        3.6
        Urban_SG56BCR
        pass
        Salmon
        auto
        reverse
        1.3
        98.7
        0.0
        Urban_SG62ACR
        pass
        RSeQC
        auto
        reverse
        0.5
        95.4
        4.1
        Urban_SG62ACR
        pass
        Salmon
        auto
        reverse
        1.5
        98.5
        0.0
        Urban_SG64BCR
        pass
        RSeQC
        auto
        reverse
        2.8
        93.8
        3.4
        Urban_SG64BCR
        pass
        Salmon
        auto
        reverse
        1.3
        98.7
        0.0
        Urban_SG78ACR
        pass
        RSeQC
        auto
        reverse
        2.4
        95.8
        1.8
        Urban_SG78ACR
        pass
        Salmon
        auto
        reverse
        1.6
        98.4
        0.0
        Urban_SG79ACR
        pass
        RSeQC
        auto
        reverse
        2.7
        93.1
        4.2
        Urban_SG79ACR
        pass
        Salmon
        auto
        reverse
        1.2
        98.8
        0.0
        Urban_SG80BCR
        pass
        RSeQC
        auto
        reverse
        3.1
        93.2
        3.7
        Urban_SG80BCR
        pass
        Salmon
        auto
        reverse
        1.5
        98.5
        0.0
        Urban_SG82ACR
        pass
        RSeQC
        auto
        reverse
        2.8
        94.1
        3.2
        Urban_SG82ACR
        pass
        Salmon
        auto
        reverse
        1.7
        98.3
        0.0
        Urban_SG84ACR
        pass
        RSeQC
        auto
        reverse
        0.5
        95.7
        3.7
        Urban_SG84ACR
        pass
        Salmon
        auto
        reverse
        1.3
        98.7
        0.0
        Urban_SG97BCR
        pass
        RSeQC
        auto
        reverse
        0.6
        95.6
        3.8
        Urban_SG97BCR
        pass
        Salmon
        auto
        reverse
        1.5
        98.5
        0.0

        FastQC (raw)

        This section of the report shows FastQC results before adapter trimming.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        010M20M30M40M50M60M70M80MUrban_SG97BCR_2Urban_SG84ACR_2Urban_SG82ACR_2Urban_SG80BCR_2Urban_SG79ACR_2Urban_SG78ACR_2Urban_SG64BCR_2Urban_SG62ACR_2Urban_SG56BCR_2Urban_SG55BCR_2Urban_SG34BCR_2Urban_SG33BCR_2Urban_SG32BCR_2Urban_SG30ACR_2Park_SG95ACR_2Park_SG94BCR_2Park_SG90ACR_2Park_SG73BCR_2Park_SG53ACR_2Park_SG48BCR_2Park_SG23ACR_2Park_SG18ACR_2Park_SG17BCR_2Park_SG109BCR_2Control_SG86ACR_2Control_SG7ACR_2Control_SG6BCR_2Control_SG68ACR_2Control_SG40ACR_2Control_SG37BCR_2
        Unique ReadsDuplicate ReadsFastQC: Sequence Counts60 samplesNumber of reads
        Created with MultiQC

        Sequence Quality Histograms
        60
        0
        0
        60
        0
        0
        60
        0
        0

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        0 bp20 bp40 bp60 bp80 bp100 bp120 bp140 bp0510152025303540
        FastQC: Mean Quality Scores60 samplesPosition (bp)Phred Score
        Created with MultiQC

        Per Sequence Quality Scores
        60
        0
        0
        60
        0
        0
        60
        0
        0

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        051015202530354005M10M15M20M25M30M35M
        FastQC: Per Sequence Quality Scores60 samplesMean Sequence Quality (Phred Score)Count
        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -
        0
        27
        33
        0
        27
        33
        0
        27
        33

        Per Sequence GC Content
        7
        36
        17
        7
        36
        17
        7
        36
        17

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        0%20%40%60%80%100%0%1%2%3%4%5%6%7%
        FastQC: Per Sequence GC ContentPercentages, 60 samples% GCPercentage
        Created with MultiQC

        Per Base N Content
        60
        0
        0
        60
        0
        0
        60
        0
        0

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        0 bp20 bp40 bp60 bp80 bp100 bp120 bp140 bp0%1%2%3%4%5%
        FastQC: Per Base N Content60 samplesPosition in Read (bp)Percentage N-Count
        Created with MultiQC

        Sequence Length Distribution
        60
        0
        0
        60
        0
        0
        60
        0
        0

        All samples have sequences of a single length (150bp)

        Sequence Duplication Levels
        0
        0
        60
        0
        0
        60
        0
        0
        60

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        123456789>10>50>100>500>1k>5k>10k+0%20%40%60%80%100%
        FastQC: Sequence Duplication Levels60 samplesSequence Duplication Level% of Library
        Created with MultiQC

        Overrepresented sequences by sample
        0
        57
        3
        0
        57
        3
        0
        57
        3

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        0%5%10%15%20%Urban_SG97BCR_2Urban_SG84ACR_2Urban_SG82ACR_2Urban_SG80BCR_2Urban_SG79ACR_2Urban_SG78ACR_2Urban_SG64BCR_2Urban_SG62ACR_2Urban_SG56BCR_2Urban_SG55BCR_2Urban_SG34BCR_2Urban_SG33BCR_2Urban_SG32BCR_2Urban_SG30ACR_2Park_SG95ACR_2Park_SG94BCR_2Park_SG90ACR_2Park_SG73BCR_2Park_SG53ACR_2Park_SG48BCR_2Park_SG23ACR_2Park_SG18ACR_2Park_SG17BCR_2Park_SG109BCR_2Control_SG86ACR_2Control_SG7ACR_2Control_SG6BCR_2Control_SG68ACR_2Control_SG40ACR_2Control_SG37BCR_2
        Top overrepresented sequenceSum of remaining overrepresented sequencesFastQC: Overrepresented sequences sample summary60 samplesPercentage of Total Sequences
        Created with MultiQC

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 0/20 rows and 3/3 columns.
        Overrepresented sequence
        Reports
        Occurrences
        % of all reads
        AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
        30
        5223269
        0.1908%
        GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
        30
        6604903
        0.2412%
        CCCCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT
        21
        1362778
        0.0498%
        CTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT
        19
        1190599
        0.0435%
        CCCCCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT
        11
        679512
        0.0248%
        TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT
        10
        593741
        0.0217%
        CCCGGTTTGGGCTTATCCGGTTTCGCTCGCCGCTACTCCCGGAATCGAGT
        8
        2304156
        0.0842%
        GCTGGTTTGGGCTCCTCCGGTTTCGCTCGCCGCTACTCCCGGAATCGAGT
        8
        802131
        0.0293%
        CCCCCCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT
        8
        482886
        0.0176%
        CCCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT
        6
        384218
        0.0140%
        CCGGTTTGGGCTTATCCGGTTTCGCTCGCCGCTACTCCCGGAATCGAGTT
        3
        718979
        0.0263%
        GGCGGATGCCTTGGCACTGGGAGCCGAAGAAAGACGTGATAAGCTGCGAA
        2
        139731
        0.0051%
        GAAAAAAGCAAGGTACAAAAGATTGTTACATAGGATAACATCGAAGGACA
        2
        102096
        0.0037%
        CCCGGTTTGGGCTGGCCCCCTTTCGCTCGCCGCTACTCGGGGGTTCGAGT
        2
        96391
        0.0035%
        TTTCGGTTTATCCATTCGTATTGGAATTTTTTTGAGAGATATTTTTCTCT
        2
        532679
        0.0195%
        CTTTTTTATTGATTGTCTTTATTTGAAACGCGCAATCTTCGTTTGCAACA
        2
        450770
        0.0165%
        CAGCCTTTTAAAGAGCCCAGTCTGTCATCGAGACGATCTTTAGCGTTTTG
        2
        119121
        0.0044%
        CTCAAGCCTTCTGTAGCTTTTCAGTTCTTTGTTTTTTGGATTCCACTCTT
        2
        278092
        0.0102%
        CTCAACTTCTCGAAAGCATAAGAGGTGAACGAATAAACATCAGAAGTTAA
        2
        769669
        0.0281%
        GATAAATCCATAAAGAAAAGGGAGGTCATGCAATCATGACAAAAGATGAA
        2
        181469
        0.0066%

        Adapter Content
        0
        2
        58
        0
        2
        58
        0
        2
        58

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Basic StatisticsPer Base Sequence QualityPer Tile Sequence QualityPer Sequence Quality ScoresPer Base Sequence ContentPer Sequence GC ContentPer Base N ContentSequence Length DistributionSequence Duplication LevelsOverrepresented SequencesAdapter ContentUrban_SG97BCR_1Urban_SG84ACR_1Urban_SG82ACR_1Urban_SG80BCR_1Urban_SG79ACR_1Urban_SG78ACR_1Urban_SG64BCR_1Urban_SG62ACR_1Urban_SG56BCR_1Urban_SG55BCR_1Urban_SG34BCR_1Urban_SG33BCR_1Urban_SG32BCR_1Urban_SG30ACR_1Park_SG95ACR_1Park_SG94BCR_1Park_SG90ACR_1Park_SG73BCR_1Park_SG53ACR_1Park_SG48BCR_1Park_SG23ACR_1Park_SG18ACR_1Park_SG17BCR_1Park_SG109BCR_1Control_SG86ACR_1Control_SG7ACR_1Control_SG6BCR_1Control_SG68ACR_1Control_SG40ACR_1Control_SG37BCR_1
        FastQC: Status Checks60 samples
        Created with MultiQC

        Cutadapt

        Finds and removes adapter sequences, primers, poly-A tails, and other types of unwanted sequences.URL: https://cutadapt.readthedocs.ioDOI: 10.14806/ej.17.1.200

        Filtered Reads

        This plot shows the number of reads (SE) / pairs (PE) removed by Cutadapt.

        010M20M30M40M50M60M70M80MUrban_SG97BCR_2Urban_SG84ACR_2Urban_SG82ACR_2Urban_SG80BCR_2Urban_SG79ACR_2Urban_SG78ACR_2Urban_SG64BCR_2Urban_SG62ACR_2Urban_SG56BCR_2Urban_SG55BCR_2Urban_SG34BCR_2Urban_SG33BCR_2Urban_SG32BCR_2Urban_SG30ACR_2Park_SG95ACR_2Park_SG94BCR_2Park_SG90ACR_2Park_SG73BCR_2Park_SG53ACR_2Park_SG48BCR_2Park_SG23ACR_2Park_SG18ACR_2Park_SG17BCR_2Park_SG109BCR_2Control_SG86ACR_2Control_SG7ACR_2Control_SG6BCR_2Control_SG68ACR_2Control_SG40ACR_2Control_SG37BCR_2
        Cutadapt: Filtered Reads60 samplesCounts
        Created with MultiQC

        Trimmed Sequence Lengths (3')

        This plot shows the number of reads with certain lengths of adapter trimmed for the 3' end.

        Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length.

        See the cutadapt documentation for more information on how these numbers are generated.

        20 bp40 bp60 bp80 bp100 bp120 bp140 bp02M4M6M8M10M12M14M16M
        Cutadapt: Lengths of Trimmed Sequences (3' end)Counts, 60 samplesLength Trimmed (bp)Count
        Created with MultiQC

        FastQC (trimmed)

        This section of the report shows FastQC results after adapter trimming.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        010M20M30M40M50M60M70M80MUrban_SG97BCR_2Urban_SG84ACR_2Urban_SG82ACR_2Urban_SG80BCR_2Urban_SG79ACR_2Urban_SG78ACR_2Urban_SG64BCR_2Urban_SG62ACR_2Urban_SG56BCR_2Urban_SG55BCR_2Urban_SG34BCR_2Urban_SG33BCR_2Urban_SG32BCR_2Urban_SG30ACR_2Park_SG95ACR_2Park_SG94BCR_2Park_SG90ACR_2Park_SG73BCR_2Park_SG53ACR_2Park_SG48BCR_2Park_SG23ACR_2Park_SG18ACR_2Park_SG17BCR_2Park_SG109BCR_2Control_SG86ACR_2Control_SG7ACR_2Control_SG6BCR_2Control_SG68ACR_2Control_SG40ACR_2Control_SG37BCR_2
        Unique ReadsDuplicate ReadsFastQC: Sequence Counts60 samplesNumber of reads
        Created with MultiQC

        Sequence Quality Histograms
        60
        0
        0
        60
        0
        0
        60
        0
        0

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        0 bp20 bp40 bp60 bp80 bp100 bp120 bp140 bp0510152025303540
        FastQC: Mean Quality Scores60 samplesPosition (bp)Phred Score
        Created with MultiQC

        Per Sequence Quality Scores
        60
        0
        0
        60
        0
        0
        60
        0
        0

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        051015202530354005M10M15M20M25M30M35M
        FastQC: Per Sequence Quality Scores60 samplesMean Sequence Quality (Phred Score)Count
        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -
        0
        0
        60
        0
        0
        60
        0
        0
        60

        Per Sequence GC Content
        5
        34
        21
        5
        34
        21
        5
        34
        21

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        0%20%40%60%80%100%0%1%2%3%4%5%6%7%
        FastQC: Per Sequence GC ContentPercentages, 60 samples% GCPercentage
        Created with MultiQC

        Per Base N Content
        60
        0
        0
        60
        0
        0
        60
        0
        0

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        0 bp20 bp40 bp60 bp80 bp100 bp120 bp140 bp0%1%2%3%4%5%
        FastQC: Per Base N Content60 samplesPosition in Read (bp)Percentage N-Count
        Created with MultiQC

        Sequence Length Distribution
        0
        60
        0
        0
        60
        0
        0
        60
        0

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        20 bp40 bp60 bp80 bp100 bp120 bp140 bp05M10M15M20M25M30M35M40M
        FastQC: Sequence Length Distribution60 samplesSequence Length (bp)Read Count
        Created with MultiQC

        Sequence Duplication Levels
        0
        0
        60
        0
        0
        60
        0
        0
        60

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        123456789>10>50>100>500>1k>5k>10k+0%20%40%60%80%100%
        FastQC: Sequence Duplication Levels60 samplesSequence Duplication Level% of Library
        Created with MultiQC

        Overrepresented sequences by sample
        0
        57
        3
        0
        57
        3
        0
        57
        3

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        0%5%10%15%20%Urban_SG97BCR_2Urban_SG84ACR_2Urban_SG82ACR_2Urban_SG80BCR_2Urban_SG79ACR_2Urban_SG78ACR_2Urban_SG64BCR_2Urban_SG62ACR_2Urban_SG56BCR_2Urban_SG55BCR_2Urban_SG34BCR_2Urban_SG33BCR_2Urban_SG32BCR_2Urban_SG30ACR_2Park_SG95ACR_2Park_SG94BCR_2Park_SG90ACR_2Park_SG73BCR_2Park_SG53ACR_2Park_SG48BCR_2Park_SG23ACR_2Park_SG18ACR_2Park_SG17BCR_2Park_SG109BCR_2Control_SG86ACR_2Control_SG7ACR_2Control_SG6BCR_2Control_SG68ACR_2Control_SG40ACR_2Control_SG37BCR_2
        Top overrepresented sequenceSum of remaining overrepresented sequencesFastQC: Overrepresented sequences sample summary60 samplesPercentage of Total Sequences
        Created with MultiQC

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 0/20 rows and 3/3 columns.
        Overrepresented sequence
        Reports
        Occurrences
        % of all reads
        AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
        30
        5207402
        0.1903%
        GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
        30
        5853567
        0.2139%
        CCCCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT
        21
        1346024
        0.0492%
        CTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT
        19
        1178989
        0.0431%
        TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT
        10
        586399
        0.0214%
        CCCCCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT
        10
        613819
        0.0224%
        GCTGGTTTGGGCTCCTCCGGTTTCGCTCGCCGCTACTCCCGGAATCGAGT
        8
        801816
        0.0293%
        CCCGGTTTGGGCTTATCCGGTTTCGCTCGCCGCTACTCCCGGAATCGAGT
        8
        2303714
        0.0842%
        CCCCCCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT
        7
        425019
        0.0155%
        CCCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT
        6
        380142
        0.0139%
        CCGGTTTGGGCTTATCCGGTTTCGCTCGCCGCTACTCCCGGAATCGAGTT
        3
        718814
        0.0263%
        TTTCGGTTTATCCATTCGTATTGGAATTTTTTTGAGAGATATTTTTCTCT
        2
        532550
        0.0195%
        CTTTTTTATTGATTGTCTTTATTTGAAACGCGCAATCTTCGTTTGCAACA
        2
        450668
        0.0165%
        CAGCCTTTTAAAGAGCCCAGTCTGTCATCGAGACGATCTTTAGCGTTTTG
        2
        119101
        0.0044%
        CTCAAGCCTTCTGTAGCTTTTCAGTTCTTTGTTTTTTGGATTCCACTCTT
        2
        278016
        0.0102%
        CCCGGTTTGGGCTGGCCCCCTTTCGCTCGCCGCTACTCGGGGGTTCGAGT
        2
        96363
        0.0035%
        GAAAAAAGCAAGGTACAAAAGATTGTTACATAGGATAACATCGAAGGACA
        2
        102078
        0.0037%
        GGCGGATGCCTTGGCACTGGGAGCCGAAGAAAGACGTGATAAGCTGCGAA
        2
        139723
        0.0051%
        CTCAACTTCTCGAAAGCATAAGAGGTGAACGAATAAACATCAGAAGTTAA
        2
        769662
        0.0281%
        GAAAAATCCAGGCACAATAAGCAGGAAAAAGAGCTGTTGCAAACGAAGAT
        2
        472649
        0.0173%

        Adapter Content
        60
        0
        0
        60
        0
        0
        60
        0
        0

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Basic StatisticsPer Base Sequence QualityPer Tile Sequence QualityPer Sequence Quality ScoresPer Base Sequence ContentPer Sequence GC ContentPer Base N ContentSequence Length DistributionSequence Duplication LevelsOverrepresented SequencesAdapter ContentUrban_SG97BCR_1Urban_SG84ACR_1Urban_SG82ACR_1Urban_SG80BCR_1Urban_SG79ACR_1Urban_SG78ACR_1Urban_SG64BCR_1Urban_SG62ACR_1Urban_SG56BCR_1Urban_SG55BCR_1Urban_SG34BCR_1Urban_SG33BCR_1Urban_SG32BCR_1Urban_SG30ACR_1Park_SG95ACR_1Park_SG94BCR_1Park_SG90ACR_1Park_SG73BCR_1Park_SG53ACR_1Park_SG48BCR_1Park_SG23ACR_1Park_SG18ACR_1Park_SG17BCR_1Park_SG109BCR_1Control_SG86ACR_1Control_SG7ACR_1Control_SG6BCR_1Control_SG68ACR_1Control_SG40ACR_1Control_SG37BCR_1
        FastQC: Status Checks60 samples
        Created with MultiQC

        DupRadar

        DupRadar provides duplication rate quality control for RNA-Seq datasets. Highly expressed genes can be expected to have a lot of duplicate reads, but high numbers of duplicates at low read counts can indicate low library complexity with technical duplication. This plot shows the general linear models - a summary of the gene duplication distributions.URL: bioconductor.org/packages/release/bioc/html/dupRadar.html

        0.010.1110100100010k100k0%20%40%60%80%100%
        0.5 RPKM1 read/bp0.5 RPKM1 read/bpDupRadar General Linear Model30 samplesexpression (reads/kbp)% duplicate reads
        Created with MultiQC

        Picard

        Tools for manipulating high-throughput sequencing data.URL: http://broadinstitute.github.io/picard

        Mark Duplicates

        Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.

        The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.

        To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:

        • READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES
        • READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)
        • READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL
        • READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES
        • READS_UNMAPPED = UNMAPPED_READS
        0%20%40%60%80%100%Urban_SG97BCRUrban_SG84ACRUrban_SG82ACRUrban_SG80BCRUrban_SG79ACRUrban_SG78ACRUrban_SG64BCRUrban_SG62ACRUrban_SG56BCRUrban_SG55BCRUrban_SG34BCRUrban_SG33BCRUrban_SG32BCRUrban_SG30ACRPark_SG95ACRPark_SG94BCRPark_SG90ACRPark_SG73BCRPark_SG53ACRPark_SG48BCRPark_SG23ACRPark_SG18ACRPark_SG17BCRPark_SG109BCRControl_SG86ACRControl_SG7ACRControl_SG6BCRControl_SG68ACRControl_SG40ACRControl_SG37BCR
        Unique PairsUnique UnpairedDuplicate Pairs OpticalDuplicate Pairs NonopticalDuplicate UnpairedPicard: Deduplication Stats30 samples# Reads
        Created with MultiQC

        QualiMap

        Quality control of alignment data and its derivatives like feature counts.URL: http://qualimap.bioinfo.cipf.esDOI: 10.1093/bioinformatics/btv566; 10.1093/bioinformatics/bts503

        Genomic origin of reads

        Classification of mapped reads as originating in exonic, intronic or intergenic regions. These can be displayed as either the number or percentage of mapped reads.

        There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

        For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. This allows mapped reads to be grouped by whether they originate in an exonic region (for QualiMap, this may include 5′ and 3′ UTR regions as well as protein-coding exons), an intron, or an intergenic region (see the Qualimap 2 documentation).

        The inferred genomic origins of RNA-seq reads are presented here as a bar graph showing either the number or percentage of mapped reads in each read dataset that have been assigned to each type of genomic region. This graph can be used to assess the proportion of useful reads in an RNA-seq experiment. That proportion can be reduced by the presence of intron sequences, especially if depletion of ribosomal RNA was used during sample preparation (Sims et al. 2014). It can also be reduced by off-target transcripts, which are detected in greater numbers at the sequencing depths needed to detect poorly-expressed transcripts (Tarazona et al. 2011).

        0%20%40%60%80%100%Urban_SG97BCRUrban_SG84ACRUrban_SG82ACRUrban_SG80BCRUrban_SG79ACRUrban_SG78ACRUrban_SG64BCRUrban_SG62ACRUrban_SG56BCRUrban_SG55BCRUrban_SG34BCRUrban_SG33BCRUrban_SG32BCRUrban_SG30ACRPark_SG95ACRPark_SG94BCRPark_SG90ACRPark_SG73BCRPark_SG53ACRPark_SG48BCRPark_SG23ACRPark_SG18ACRPark_SG17BCRPark_SG109BCRControl_SG86ACRControl_SG7ACRControl_SG6BCRControl_SG68ACRControl_SG40ACRControl_SG37BCR
        ExonicIntronicIntergenicQualiMap: RNAseq: Genomic Origin30 samplesNumber of reads
        Created with MultiQC

        Gene Coverage Profile

        Mean distribution of coverage depth across the length of all mapped transcripts.

        There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

        For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. QualiMap uses this information to calculate the depth of coverage along the length of each annotated transcript. For a set of reads mapped to a transcript, the depth of coverage at a given base position is the number of high-quality reads that map to the transcript at that position (Sims et al. 2014).

        QualiMap calculates coverage depth at every base position of each annotated transcript. To enable meaningful comparison between transcripts, base positions are rescaled to relative positions expressed as percentage distance along each transcript (0%, 1%, …, 99%). For the set of transcripts with at least one mapped read, QualiMap plots the cumulative mapped-read depth (y-axis) at each relative transcript position (x-axis). This plot shows the gene coverage profile across all mapped transcripts for each read dataset. It provides a visual way to assess positional biases, such as an accumulation of mapped reads at the 3′ end of transcripts, which may indicate poor RNA quality in the original sample (Conesa et al. 2016).

        The Normalised plot is calculated by MultiQC to enable comparison of samples with varying sequencing depth. The cumulative mapped-read depth at each position across the averaged transcript position are divided by the total for that sample across the entire averaged transcript.

        0%20%40%60%80%100%050k100k150k200k
        QualiMap: RNAseq: Coverage Profile Along Genes (total)Counts, 30 samplesTranscript Position (%)Cumulative mapped-read depth
        Created with MultiQC

        RSeQC

        Evaluates high throughput RNA-seq data.URL: http://rseqc.sourceforge.netDOI: 10.1093/bioinformatics/bts356

        Read Distribution

        Read Distribution calculates how mapped reads are distributed over genome features.

        0%20%40%60%80%100%Urban_SG97BCRUrban_SG84ACRUrban_SG82ACRUrban_SG80BCRUrban_SG79ACRUrban_SG78ACRUrban_SG64BCRUrban_SG62ACRUrban_SG56BCRUrban_SG55BCRUrban_SG34BCRUrban_SG33BCRUrban_SG32BCRUrban_SG30ACRPark_SG95ACRPark_SG94BCRPark_SG90ACRPark_SG73BCRPark_SG53ACRPark_SG48BCRPark_SG23ACRPark_SG18ACRPark_SG17BCRPark_SG109BCRControl_SG86ACRControl_SG7ACRControl_SG6BCRControl_SG68ACRControl_SG40ACRControl_SG37BCR
        CDS_Exons5'UTR_Exons3'UTR_ExonsIntronsTSS_up_1kbTSS_up_1kb-5kbTSS_up_5kb-10kbTES_down_1kbTES_down_1kb-5kbTES_down_5kb-10kbOther_intergenicRSeQC: Read Distribution30 samples# Tags
        Created with MultiQC

        Inner Distance

        Inner Distance calculates the inner distance (or insert size) between two paired RNA reads. Note that this can be negative if fragments overlap.

        −200 bp−150 bp−100 bp−50 bp0 bp50 bp100 bp150 bp200 bp010k20k30k40k50k60k
        RSeQC: Inner DistanceCounts, 30 samplesInner Distance (bp)Counts
        Created with MultiQC

        Read Duplication

        read_duplication.py calculates how many alignment positions have a certain number of exact duplicates. Note - plot truncated at 500 occurrences and binned.

        50100150200250300350400450500110100100010k100k1M10M
        RSeQC: Read Duplication30 samplesOccurrence of readNumber of Reads (log10)
        Created with MultiQC

        Junction Annotation

        Junction annotation compares detected splice junctions to a reference gene model. An RNA read can be spliced 2 or more times, each time is called a splicing event.

        0%20%40%60%80%100%Urban_SG97BCRUrban_SG84ACRUrban_SG82ACRUrban_SG80BCRUrban_SG79ACRUrban_SG78ACRUrban_SG64BCRUrban_SG62ACRUrban_SG56BCRUrban_SG55BCRUrban_SG34BCRUrban_SG33BCRUrban_SG32BCRUrban_SG30ACRPark_SG95ACRPark_SG94BCRPark_SG90ACRPark_SG73BCRPark_SG53ACRPark_SG48BCRPark_SG23ACRPark_SG18ACRPark_SG17BCRPark_SG109BCRControl_SG86ACRControl_SG7ACRControl_SG6BCRControl_SG68ACRControl_SG40ACRControl_SG37BCR
        Known Splicing JunctionsPartial Novel Splicing JunctionsNovel Splicing JunctionsRSeQC: Splicing JunctionsJunctions, 30 samples% Junctions
        Created with MultiQC

        Junction Saturation

        Junction Saturation counts the number of known splicing junctions that are observed in each dataset. If sequencing depth is sufficient, all (annotated) splice junctions should be rediscovered, resulting in a curve that reaches a plateau. Missing low abundance splice junctions can affect downstream analysis.

        Click a line to see the data side by side (as in the original RSeQC plot).

        0%20%40%60%80%100%050k100k150k200k250k
        RSeQC: Junction SaturationAll Junctions, 30 samplesPercent of readsNumber of Junctions
        Created with MultiQC

        Infer experiment

        Infer experiment counts the percentage of reads and read pairs that match the strandedness of overlapping transcripts. It can be used to infer whether RNA-seq library preps are stranded (sense or antisense).

        0%20%40%60%80%100%Urban_SG97BCRUrban_SG84ACRUrban_SG82ACRUrban_SG80BCRUrban_SG79ACRUrban_SG78ACRUrban_SG64BCRUrban_SG62ACRUrban_SG56BCRUrban_SG55BCRUrban_SG34BCRUrban_SG33BCRUrban_SG32BCRUrban_SG30ACRPark_SG95ACRPark_SG94BCRPark_SG90ACRPark_SG73BCRPark_SG53ACRPark_SG48BCRPark_SG23ACRPark_SG18ACRPark_SG17BCRPark_SG109BCRControl_SG86ACRControl_SG7ACRControl_SG6BCRControl_SG68ACRControl_SG40ACRControl_SG37BCR
        SenseAntisenseUndeterminedRSeQC: Infer experiment30 samples% Tags
        Created with MultiQC

        Bam Stat

        All numbers reported in millions.

        0M20M40M60M80M100M120M140MTotal records 0M20M40M60M80M100M120M140MQC failed 0M20M40M60M80M100M120M140MDuplicates 0M20M40M60M80M100M120M140MNon primary hit 0M20M40M60M80M100M120M140MUnmapped 0M20M40M60M80M100M120M140MUnique 0M20M40M60M80M100M120M140MRead-1 0M20M40M60M80M100M120M140MRead-2 0M20M40M60M80M100M120M140M+ve strand 0M20M40M60M80M100M120M140M-ve strand 0M20M40M60M80M100M120M140MNon-splice reads 0M20M40M60M80M100M120M140MSplice reads 0M20M40M60M80M100M120M140MProper pairs 0M20M40M60M80M100M120M140MDifferent chrom
        RSeQC: Bam Stat30 samples
        Created with MultiQC

        Samtools

        Toolkit for interacting with BAM/CRAM files.URL: http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352

        Percent mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

        For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

        Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

        Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

        020M40M60M80M100M120MUrban_SG97BCRUrban_SG84ACRUrban_SG82ACRUrban_SG80BCRUrban_SG79ACRUrban_SG78ACRUrban_SG64BCRUrban_SG62ACRUrban_SG56BCRUrban_SG55BCRUrban_SG34BCRUrban_SG33BCRUrban_SG32BCRUrban_SG30ACRPark_SG95ACRPark_SG94BCRPark_SG90ACRPark_SG73BCRPark_SG53ACRPark_SG48BCRPark_SG23ACRPark_SG18ACRPark_SG17BCRPark_SG109BCRControl_SG86ACRControl_SG7ACRControl_SG6BCRControl_SG68ACRControl_SG40ACRControl_SG37BCR
        Mapped (with MQ>0)MQ0Samtools: stats: Alignment Scores30 samples# Reads
        Created with MultiQC

        Alignment stats

        This module parses the output from samtools stats. All numbers in millions.

        0M20M40M60M80M100M120MTotal sequences 0M20M40M60M80M100M120MMapped & paired 0M20M40M60M80M100M120MProperly paired 0M20M40M60M80M100M120MDuplicated 0M20M40M60M80M100M120MQC Failed 0M20M40M60M80M100M120MReads MQ0 0Mb2kMb4kMb6kMb8kMb10kMb12kMb14kMb16kMb18kMbMapped bases (CIGAR) 0Mb2kMb4kMb6kMb8kMb10kMb12kMb14kMb16kMb18kMbBases Trimmed 0Mb2kMb4kMb6kMb8kMb10kMb12kMb14kMb16kMb18kMbDuplicated bases 0M20M40M60M80M100M120MDiff chromosomes 0M20M40M60M80M100M120MOther orientation 0M20M40M60M80M100M120MInward pairs 0M20M40M60M80M100M120MOutward pairs
        Samtools: stats: Alignment Stats30 samples
        Created with MultiQC

        Flagstat

        This module parses the output from samtools flagstat

        0 M20 M40 M60 M80 M100 M120 M140 MTotal Reads 0 M20 M40 M60 M80 M100 M120 M140 MTotal Passed QC 0 M20 M40 M60 M80 M100 M120 M140 MMapped 0 M20 M40 M60 M80 M100 M120 M140 MSecondary Alignments 0 M20 M40 M60 M80 M100 M120 M140 MDuplicates 0 M20 M40 M60 M80 M100 M120 M140 MPaired in Sequencing 0 M20 M40 M60 M80 M100 M120 M140 MProperly Paired 0 M20 M40 M60 M80 M100 M120 M140 MSelf and mate mapped 0 M20 M40 M60 M80 M100 M120 M140 MSingletons 0 M20 M40 M60 M80 M100 M120 M140 MMate mapped to diff chr 0 M20 M40 M60 M80 M100 M120 M140 MDiff chr (mapQ >= 5)
        Samtools flagstat: read countRead counts, 30 samples
        Created with MultiQC

        Mapped reads per contig

        The samtools idxstats tool counts the number of mapped reads per chromosome / contig. Chromosomes with < 0.1% of the total aligned reads are omitted from this plot.

        NW_025965008.1NW_025965021.1NW_025965028.1NW_025965039.1NW_025965049.1NW_025965057.1NW_025965059.1NW_025965066.1NW_025965072.1NW_025965075.1NW_025965080.1NW_025965091.1NW_025965099.1NW_025965103.1NW_025965112.1NW_025965118.1NW_025965127.1NW_025965131.1NW_025965135.1NW_025965137.1NW_025965144.1NW_025965148.1NW_025965151.1NW_025965155.1NW_025965161.1NW_025965166.1NW_025965175.1NW_025965178.1NW_025965182.1NW_025965190.1NW_025965192.1NW_025965195.1NW_025965203.1NW_025965207.1NW_025965212.1NW_025965218.1NW_025965223.1NW_025965227.1NW_025965230.1NW_025965235.1NW_025965238.1NW_025965242.1NW_025965247.1NW_025965251.1NW_025965253.1NW_025965257.1NW_025965259.1NW_025965261.1NW_025965266.1NW_025965271.1NW_025965275.1NW_025965277.1NW_025965279.1NW_025965281.1NW_025965284.1NW_025965286.1NW_025965288.1NW_025965290.1NW_025965295.1NW_025965297.1NW_025965299.1NW_025965301.1NW_025965303.1NW_025965305.1NW_025965307.1NW_025965309.1NW_025965311.1NW_025965313.1NW_025965315.1NW_025965317.1NW_025965319.1NW_025965321.1NW_025965323.1NW_025965326.1NW_025965341.1NW_025965345.1NW_025965347.1NW_025965352.1NW_025965359.1NW_025965367.1NW_025965401.1NW_025965418.1NW_025965426.1NW_025965432.1NC_024538.100.020.040.060.080.1
        Samtools: idxstats: Mapped reads per contigNormalised Counts, 30 samplesChromosome nameFraction of total count
        Created with MultiQC

        STAR

        Universal RNA-seq aligner.URL: https://github.com/alexdobin/STARDOI: 10.1093/bioinformatics/bts635

        Summary Statistics

        Summary statistics from the STAR alignment

        Showing 0/30 rows and 10/19 columns.
        Sample Name
        Total reads
        Aligned
        Aligned
        Uniq aligned
        Uniq aligned
        Multimapped
        Avg. read len
        Avg. mapped len
        Splices
        Annotated splices
        GT/AG splices
        GC/AG splices
        AT/AC splices
        Non-canonical splices
        Mismatch rate
        Del rate
        Del len
        Ins rate
        Ins len
        Control_SG37BCR
        32.1M
        29.6M
        92.3%
        28.5M
        88.7%
        1.2M
        286.0bp
        284.6bp
        29.9M
        29.9M
        29.6M
        0.3M
        0.0M
        0.1M
        0.6%
        0.0%
        2.4bp
        0.0%
        2.1bp
        Control_SG40ACR
        36.3M
        29.9M
        82.3%
        28.5M
        78.6%
        1.4M
        285.0bp
        285.1bp
        23.8M
        23.8M
        23.5M
        0.2M
        0.0M
        0.0M
        0.7%
        0.0%
        2.5bp
        0.0%
        2.1bp
        Control_SG68ACR
        48.6M
        41.9M
        86.1%
        40.2M
        82.6%
        1.7M
        285.0bp
        284.6bp
        41.8M
        41.8M
        41.3M
        0.4M
        0.0M
        0.1M
        0.6%
        0.0%
        2.5bp
        0.0%
        2.0bp
        Control_SG6BCR
        34.0M
        19.9M
        58.5%
        19.0M
        55.7%
        0.9M
        284.0bp
        285.2bp
        18.8M
        18.8M
        18.6M
        0.1M
        0.0M
        0.0M
        0.6%
        0.0%
        2.5bp
        0.0%
        2.1bp
        Control_SG7ACR
        41.4M
        33.7M
        81.5%
        32.0M
        77.4%
        1.7M
        280.0bp
        280.1bp
        31.1M
        31.0M
        30.7M
        0.3M
        0.0M
        0.1M
        0.6%
        0.0%
        2.5bp
        0.0%
        2.0bp
        Control_SG86ACR
        34.6M
        30.0M
        86.8%
        28.9M
        83.7%
        1.1M
        287.0bp
        286.4bp
        29.5M
        29.5M
        29.1M
        0.3M
        0.0M
        0.1M
        0.6%
        0.0%
        2.5bp
        0.0%
        2.0bp
        Park_SG109BCR
        47.0M
        42.5M
        90.5%
        40.4M
        86.1%
        2.1M
        287.0bp
        285.8bp
        35.9M
        35.9M
        35.5M
        0.4M
        0.0M
        0.1M
        0.6%
        0.0%
        2.5bp
        0.0%
        2.1bp
        Park_SG17BCR
        39.5M
        35.0M
        88.4%
        33.6M
        85.1%
        1.3M
        291.0bp
        289.7bp
        36.9M
        36.8M
        36.4M
        0.3M
        0.0M
        0.1M
        0.6%
        0.0%
        2.4bp
        0.0%
        2.0bp
        Park_SG18ACR
        46.6M
        42.6M
        91.5%
        41.0M
        88.0%
        1.6M
        287.0bp
        285.3bp
        36.3M
        36.2M
        35.8M
        0.3M
        0.0M
        0.1M
        0.6%
        0.0%
        2.5bp
        0.0%
        2.1bp
        Park_SG23ACR
        41.5M
        38.5M
        92.7%
        37.0M
        89.1%
        1.5M
        288.0bp
        286.5bp
        39.1M
        39.1M
        38.7M
        0.4M
        0.0M
        0.1M
        0.6%
        0.0%
        2.5bp
        0.0%
        2.0bp
        Park_SG48BCR
        37.2M
        26.9M
        72.4%
        25.7M
        69.0%
        1.3M
        284.0bp
        285.0bp
        27.3M
        27.2M
        27.0M
        0.2M
        0.0M
        0.0M
        0.6%
        0.0%
        2.3bp
        0.0%
        2.0bp
        Park_SG53ACR
        35.6M
        22.0M
        61.8%
        21.0M
        59.1%
        1.0M
        283.0bp
        285.2bp
        22.7M
        22.7M
        22.4M
        0.2M
        0.0M
        0.0M
        0.6%
        0.0%
        2.4bp
        0.0%
        2.0bp
        Park_SG73BCR
        79.8M
        66.0M
        82.7%
        62.5M
        78.3%
        3.5M
        278.0bp
        278.5bp
        63.2M
        63.1M
        62.5M
        0.5M
        0.1M
        0.1M
        0.6%
        0.0%
        2.4bp
        0.0%
        2.1bp
        Park_SG90ACR
        25.6M
        12.7M
        49.7%
        12.1M
        47.2%
        0.6M
        282.0bp
        286.2bp
        10.9M
        10.9M
        10.8M
        0.1M
        0.0M
        0.0M
        0.6%
        0.0%
        2.5bp
        0.0%
        2.1bp
        Park_SG94BCR
        47.1M
        43.9M
        93.2%
        42.2M
        89.6%
        1.7M
        286.0bp
        284.8bp
        43.7M
        43.7M
        43.2M
        0.4M
        0.0M
        0.1M
        0.6%
        0.0%
        2.5bp
        0.0%
        2.0bp
        Park_SG95ACR
        35.2M
        31.9M
        90.7%
        30.7M
        87.1%
        1.2M
        283.0bp
        282.0bp
        32.4M
        32.4M
        32.0M
        0.3M
        0.0M
        0.1M
        0.6%
        0.0%
        2.5bp
        0.0%
        2.0bp
        Urban_SG30ACR
        40.2M
        30.2M
        75.2%
        28.9M
        72.0%
        1.3M
        286.0bp
        285.4bp
        31.4M
        31.4M
        31.0M
        0.3M
        0.0M
        0.1M
        0.6%
        0.0%
        2.4bp
        0.0%
        2.0bp
        Urban_SG32BCR
        34.4M
        30.9M
        89.7%
        29.5M
        85.7%
        1.4M
        283.0bp
        281.8bp
        29.7M
        29.7M
        29.4M
        0.2M
        0.0M
        0.1M
        0.6%
        0.0%
        2.4bp
        0.0%
        2.0bp
        Urban_SG33BCR
        36.5M
        32.9M
        90.1%
        31.7M
        86.7%
        1.2M
        288.0bp
        286.9bp
        32.8M
        32.8M
        32.4M
        0.3M
        0.0M
        0.1M
        0.6%
        0.0%
        2.5bp
        0.0%
        2.0bp
        Urban_SG34BCR
        54.5M
        49.6M
        91.1%
        47.5M
        87.2%
        2.1M
        283.0bp
        281.3bp
        46.6M
        46.5M
        46.0M
        0.4M
        0.0M
        0.1M
        0.6%
        0.0%
        2.5bp
        0.0%
        2.1bp
        Urban_SG55BCR
        44.0M
        35.5M
        80.7%
        33.8M
        76.9%
        1.7M
        286.0bp
        285.9bp
        27.1M
        27.1M
        26.7M
        0.3M
        0.0M
        0.0M
        0.7%
        0.0%
        2.5bp
        0.0%
        2.1bp
        Urban_SG56BCR
        39.0M
        34.3M
        88.1%
        32.9M
        84.3%
        1.5M
        285.0bp
        284.7bp
        33.9M
        33.8M
        33.5M
        0.3M
        0.0M
        0.1M
        0.5%
        0.0%
        2.4bp
        0.0%
        2.0bp
        Urban_SG62ACR
        49.8M
        40.3M
        80.9%
        38.6M
        77.5%
        1.7M
        285.0bp
        284.5bp
        41.4M
        41.3M
        40.9M
        0.4M
        0.0M
        0.1M
        0.6%
        0.0%
        2.5bp
        0.0%
        2.0bp
        Urban_SG64BCR
        40.3M
        34.4M
        85.3%
        32.8M
        81.3%
        1.6M
        274.0bp
        275.3bp
        32.7M
        32.7M
        32.3M
        0.3M
        0.0M
        0.1M
        0.5%
        0.0%
        2.5bp
        0.0%
        2.0bp
        Urban_SG78ACR
        38.8M
        14.7M
        38.0%
        14.0M
        36.1%
        0.7M
        279.0bp
        281.2bp
        14.6M
        14.6M
        14.5M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.5bp
        0.0%
        2.0bp
        Urban_SG79ACR
        33.9M
        31.4M
        92.9%
        30.0M
        88.6%
        1.5M
        287.0bp
        286.0bp
        31.2M
        31.2M
        30.9M
        0.3M
        0.0M
        0.1M
        0.6%
        0.0%
        2.4bp
        0.0%
        2.2bp
        Urban_SG80BCR
        36.5M
        33.1M
        90.6%
        31.5M
        86.4%
        1.5M
        287.0bp
        285.6bp
        25.9M
        25.9M
        25.6M
        0.2M
        0.0M
        0.0M
        0.6%
        0.0%
        2.6bp
        0.0%
        2.1bp
        Urban_SG82ACR
        27.1M
        18.7M
        69.0%
        17.9M
        65.9%
        0.8M
        283.0bp
        283.9bp
        18.6M
        18.6M
        18.4M
        0.2M
        0.0M
        0.0M
        0.6%
        0.0%
        2.4bp
        0.0%
        2.1bp
        Urban_SG84ACR
        47.5M
        37.8M
        79.5%
        36.2M
        76.2%
        1.5M
        283.0bp
        282.7bp
        39.7M
        39.7M
        39.3M
        0.4M
        0.0M
        0.1M
        0.6%
        0.0%
        2.6bp
        0.0%
        2.0bp
        Urban_SG97BCR
        44.7M
        39.4M
        88.2%
        37.7M
        84.4%
        1.7M
        282.0bp
        281.3bp
        39.3M
        39.3M
        38.9M
        0.3M
        0.0M
        0.1M
        0.6%
        0.0%
        2.5bp
        0.0%
        2.0bp

        Alignment Scores

        010M20M30M40M50M60M70MUrban_SG97BCRUrban_SG84ACRUrban_SG82ACRUrban_SG80BCRUrban_SG79ACRUrban_SG78ACRUrban_SG64BCRUrban_SG62ACRUrban_SG56BCRUrban_SG55BCRUrban_SG34BCRUrban_SG33BCRUrban_SG32BCRUrban_SG30ACRPark_SG95ACRPark_SG94BCRPark_SG90ACRPark_SG73BCRPark_SG53ACRPark_SG48BCRPark_SG23ACRPark_SG18ACRPark_SG17BCRPark_SG109BCRControl_SG86ACRControl_SG7ACRControl_SG6BCRControl_SG68ACRControl_SG40ACRControl_SG37BCR
        Uniquely mappedMapped to multiple lociMapped to too many lociUnmapped: too shortUnmapped: otherSTAR: Alignment Scores30 samples# Reads
        Created with MultiQC

        Sample relationships

        Plots interrogating sample relationships, based on final count matrices.

        STAR_SALMON DESeq2 sample similarity

        Control_SG37BCRControl_SG40ACRControl_SG68ACRControl_SG6BCRControl_SG7ACRControl_SG86ACRPark_SG109BCRPark_SG17BCRPark_SG18ACRPark_SG23ACRPark_SG48BCRPark_SG53ACRPark_SG73BCRPark_SG90ACRPark_SG94BCRPark_SG95ACRUrban_SG30ACRUrban_SG32BCRUrban_SG33BCRUrban_SG34BCRUrban_SG55BCRUrban_SG56BCRUrban_SG62ACRUrban_SG64BCRUrban_SG78ACRUrban_SG79ACRUrban_SG80BCRUrban_SG82ACRUrban_SG84ACRUrban_SG97BCRUrban_SG97BCRUrban_SG84ACRUrban_SG82ACRUrban_SG80BCRUrban_SG79ACRUrban_SG78ACRUrban_SG64BCRUrban_SG62ACRUrban_SG56BCRUrban_SG55BCRUrban_SG34BCRUrban_SG33BCRUrban_SG32BCRUrban_SG30ACRPark_SG95ACRPark_SG94BCRPark_SG90ACRPark_SG73BCRPark_SG53ACRPark_SG48BCRPark_SG23ACRPark_SG18ACRPark_SG17BCRPark_SG109BCRControl_SG86ACRControl_SG7ACRControl_SG6BCRControl_SG68ACRControl_SG40ACRControl_SG37BCR
        020406080100120140DESeq2: Heatmap of the sample-to-sample distances30 samples
        Created with MultiQC

        STAR_SALMON DESeq2 PCA plot

        −60−40−2002040−20020406080
        DESeq2: Principal component plot30 samplesPC1PC2
        Created with MultiQC

        SortMeRNA

        Program for filtering, mapping and OTU-picking NGS reads in metatranscriptomic and metagenomic data.URL: http://bioinfo.lifl.fr/RNA/sortmernaDOI: 10.1093/bioinformatics/bts611

        The core algorithm is based on approximate seeds and allows for fast and sensitive analyses of nucleotide sequences. The main application of SortMeRNA is filtering ribosomal RNA from metatranscriptomic data.
        05M10M15M20M25MUrban_SG97BCR_2Urban_SG84ACR_2Urban_SG82ACR_2Urban_SG80BCR_2Urban_SG79ACR_2Urban_SG78ACR_2Urban_SG64BCR_2Urban_SG62ACR_2Urban_SG56BCR_2Urban_SG55BCR_2Urban_SG34BCR_2Urban_SG33BCR_2Urban_SG32BCR_2Urban_SG30ACR_2Park_SG95ACR_2Park_SG94BCR_2Park_SG90ACR_2Park_SG73BCR_2Park_SG53ACR_2Park_SG48BCR_2Park_SG23ACR_2Park_SG18ACR_2Park_SG17BCR_2Park_SG109BCR_2Control_SG86ACR_2Control_SG7ACR_2Control_SG6BCR_2Control_SG68ACR_2Control_SG40ACR_2Control_SG37BCR_2
        rfam-5.8s-database-id98_countrfam-5s-database-id98_countsilva-arc-16s-id95_countsilva-arc-23s-id98_countsilva-bac-16s-id90_countsilva-bac-23s-id98_countsilva-euk-18s-id95_countsilva-euk-28s-id98_countSortMeRNA: Hit Counts30 samplesReads
        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        BEDTOOLS_GENOMECOV_FWbedtools2.31.1
        CUSTOM_GETCHROMSIZESgetchromsizes1.21
        CUSTOM_TX2GENEpython3.10.4
        DESEQ2_QC_STAR_SALMONbioconductor-deseq21.28.0
        r-base4.0.3
        DupRadarbioconductor-dupradar1.32.0
        FASTQCfastqc0.12.1
        FQ_LINTfq0.12.0 (2024-07-08)
        FQ_SUBSAMPLEfq0.12.0 (2024-07-08)
        GTF2BEDperl5.26.2
        GTF_FILTERpython3.9.5
        MAKE_TRANSCRIPTS_FASTArsem1.3.1
        star2.7.10a
        PICARD_MARKDUPLICATESpicard3.1.1
        QUALIMAP_RNASEQqualimap2.3
        RSEQC_BAMSTATrseqc5.0.2
        RSEQC_INFEREXPERIMENTrseqc5.0.2
        RSEQC_INNERDISTANCErseqc5.0.2
        RSEQC_JUNCTIONANNOTATIONrseqc5.0.2
        RSEQC_JUNCTIONSATURATIONrseqc5.0.2
        RSEQC_READDISTRIBUTIONrseqc5.0.2
        RSEQC_READDUPLICATIONrseqc5.0.2
        SALMON_INDEXsalmon1.10.3
        SALMON_QUANTsalmon1.10.3
        SAMTOOLS_FLAGSTATsamtools1.21
        SAMTOOLS_IDXSTATSsamtools1.21
        SAMTOOLS_INDEXsamtools1.21
        SAMTOOLS_SORTsamtools1.21
        SAMTOOLS_STATSsamtools1.21
        SE_GENEbioconductor-summarizedexperiment1.32.0
        SORTMERNA_INDEXsortmerna4.3.7
        STAR_ALIGNgawk5.1.0
        samtools1.21
        star2.7.11b
        STAR_GENOMEGENERATEgawk5.1.0
        samtools1.21
        star2.7.11b
        STRINGTIE_STRINGTIEstringtie2.2.3
        SortMeRNAsortmerna4.3.7
        TRIMGALOREcutadapt4.9
        trimgalore0.6.10
        TXIMETA_TXIMPORTbioconductor-tximeta1.20.1
        UCSC_BEDCLIPucsc377
        UCSC_BEDGRAPHTOBIGWIGucsc469
        WorkflowNextflow24.10.3
        nf-core/rnaseqv3.18.0-gb96a753

        nf-core/rnaseq Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/nf-core/rnaseq

        Methods

        Data was processed using nf-core/rnaseq v3.18.0 (doi: 10.5281/zenodo.1400710) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v24.10.3 (Di Tommaso et al., 2017) with the following command:

        nextflow run nf-core/rnaseq -r 3.18.0 -profile apptainer -work-dir /scratch/project_2003826/SOIL2GUT_RNA/work -params-file /scratch/project_2003826/SOIL2GUT_RNA/nf-params.json --igenomes_ignore -resume

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        nf-core/rnaseq Workflow Summary

        - this information is collected when the pipeline is started.URL: https://github.com/nf-core/rnaseq

        Input/output options

        email
        redacted
        input
        /scratch/project_2003826/samplesheet.csv
        multiqc_title
        redacted
        outdir
        /scratch/project_2003826/results

        Reference genome options

        fasta
        /scratch/project_2003826/reference/genome.fa
        gtf
        /scratch/project_2003826/reference/annotation.gtf
        igenomes_ignore
        true

        Read filtering options

        remove_ribo_rna
        true

        Core Nextflow options

        configFiles
        N/A
        containerEngine
        apptainer
        launchDir
        /scratch/project_2003826/
        profile
        apptainer
        projectDir
        /users/tjernfor/.nextflow/assets/nf-core/rnaseq
        revision
        3.18.0
        runName
        stupefied_hilbert
        userName
        tjernfor
        workDir
        /scratch/project_2003826/work